Localization of macromolecules in crowded cellular cryo-electron tomograms from extremely sparse labels
Uddin, Mostofa Rafid Ahmed, Ajmain Yasar Tabib, H. M. Shadman Tahmid, Md Toki Alam, Md Zarif Ul Freyberg, Zachary Xu, Min
Uddin, Mostofa Rafid
Ahmed, Ajmain Yasar
Tabib, H. M. Shadman
Tahmid, Md Toki
Alam, Md Zarif Ul
Freyberg, Zachary
Xu, Min
Supervisor
Department
Computer Vision
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Type
Journal article
Date
2025
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Language
English
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Abstract
Localizing macromolecules in crowded cellular cryo-electron tomography (cryo-ET) images or tomograms is crucial for determining their in situ structures. Traditional template matching-based approaches for this task suffer from template-specific biases and have low throughput. Given these problems, learning-based solutions are necessary. However, the paucity of annotated data for training poses substantial challenges for such learning-based methods. Moreover, preparing extensively annotated cellular tomograms for training macromolecule localization methods is extremely time-consuming and burdensome due to the large volume and low signal-to-noise ratio of the tomograms. In this work, we developed TomoPicker, an annotation-efficient macromolecule localization method for tomograms. To achieve such annotation-efficiency, TomoPicker regards macromolecule localization as a voxel classification problem and solves it with two different positive-unlabeled learning approaches. We evaluated TomoPicker on two experimental cryo-ET datasets of crowded eukaryotic cells and one experimental dataset of relatively less crowded prokaryotic cell. We observed that, with only 10 annotated macromolecule locations, TomoPicker with positive unlabeled learning achieved a performance comparable to that of state-of-the-art supervised methods trained with several hundred annotations. In other words, TomoPicker achieved plausible segmentation with up to 98% less data compared with supervised learning-based methods. Furthermore, it demonstrated substantial improvements over existing learning-based macromolecule localization methods under sparse annotation scenarios.
Citation
M. R. Uddin et al., “Localization of macromolecules in crowded cellular cryo-electron tomograms from extremely sparse labels,” Brief Bioinform, vol. 26, no. 6, Nov. 2025, doi: 10.1093/BIB/BBAF630.
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Briefings in Bioinformatics
Conference
Keywords
Cryo-Electron Tomography, Macromolecule Localization, Positive-Unlabeled Learning, Three-Dimensional Classification, Cell and Structural Biology
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Oxford University Press